@article{f10bd00da360446aa0b4f582ca36d6f1,
title = "Modeling the Solvation and Acidity of Carboxylic Acids Using an Ab Initio Deep Neural Network Potential",
abstract = "Formic and acetic acid constitute the simplest of carboxylic acids, yet they exhibit fascinating chemistry in the condensed phase such as proton transfer and dimerization. The go-to method of choice for modeling these rare events have been accurate but expensive ab initio molecular dynamics simulations. In this study, we present a deep neural network potential trained using accurate ab initio data that can be used in tandem with enhanced-sampling methods to perform an efficient exploration of the free-energy surface of aqueous solutions of weak carboxylic acids. In particular, we show that our model captures proton dissociation and provides a good estimate of the pKa, as well as the dimerization of formic and acetic acid. This provides a suitable starting point for applications in different research areas where computational efficiency coupled with the accuracy of ab initio methods is required.",
author = "Raman, {Abhinav S.} and Annabella Selloni",
note = "Funding Information: This work was supported by DoE BES, CSGB Division under Award DESC0007347, with further support from the Computational Chemical Center: Chemistry in Solution and at Interfaces, funded by DoE under Award DESC0019394. The authors acknowledge the use of computational resources provided by the Extreme Science and Engineering Discovery Environment (XSEDE) supported though the National Science Foundation (NSF) under award number CHE210068 and CHE210079, and the National Energy Research Scientific Computing Center (NERSC), under DoE Contract No. DE-AC02-05cH11231. We also acknowledge the use of the Princeton Research Computing resources at Princeton University which is a consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology{\textquoteright}s Research Computing. We thank Pablo Piaggi and Athanassios Panagiotopoulos for helpful discussions. Publisher Copyright: {\textcopyright} 2022 American Chemical Society.",
year = "2022",
month = oct,
day = "13",
doi = "10.1021/acs.jpca.2c06252",
language = "English (US)",
volume = "126",
pages = "7283--7290",
journal = "Journal of Physical Chemistry A",
issn = "1089-5639",
publisher = "American Chemical Society",
number = "40",
}